{"id":"W1980538968","doi":"10.1002/cplx.20100","title":"Modeling pathways of differentiation in genetic regulatory networks with Boolean networks","year":2005,"lang":"en","type":"article","venue":"Complexity","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institutes of Health; National Science Foundation","keywords":"Attractor; Observable; Cellular differentiation; Nonlinear system; Perturbation (astronomy); Computer science; Gene regulatory network; Variety (cybernetics); Gene; Topology (electrical circuits); Biology; Biological system; Mathematics; Physics; Genetics; Gene expression; Artificial intelligence; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000172439,0.0001933935,0.0002700691,0.00006939558,0.00006052274,0.00001221626,0.0002093178,0.0001558064,0.0000200346],"category_scores_gemma":[0.000006672626,0.0001850837,0.0001066941,0.0001922026,0.0001104668,0.000004923873,0.0001047661,0.0001200953,0.000001065289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003571429,"about_ca_system_score_gemma":0.00003736806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002698188,"about_ca_topic_score_gemma":0.001063279,"domain_scores_codex":[0.9986771,0.0001007658,0.00037127,0.0003819912,0.0001595379,0.0003092891],"domain_scores_gemma":[0.999146,0.000006075906,0.0001260616,0.0005545064,0.00008559447,0.00008170409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006822793,0.00006596174,0.02439829,0.000008381385,0.00005430497,8.201013e-7,0.0000245929,0.9682709,0.003936937,0.0001071858,0.00005946402,0.003004953],"study_design_scores_gemma":[0.0004603788,0.00007631703,0.08611763,0.0000240012,0.00003037473,0.000004086509,0.00002135952,0.9114164,0.001466107,0.0001068051,0.00007801714,0.0001985338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7888423,0.00148163,0.2093994,0.00001729173,0.00003051096,0.0001181675,0.00000195883,0.00001185983,0.000096924],"genre_scores_gemma":[0.9958103,0.0001066277,0.003479954,0.0000559394,0.000358055,0.00001100007,0.000117032,0.00002945935,0.00003158173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2069681,"threshold_uncertainty_score":0.7547495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02102476204328716,"score_gpt":0.2190382291684405,"score_spread":0.1980134671251533,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}